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Multimodal and Multitask Approach to Listener’s Backchannel Prediction: Can Prediction of Turn-changing and Turn-management Willingness Improve Backchannel Modeling
- Award ID(s):
- 1750439
- PAR ID:
- 10317283
- Date Published:
- Journal Name:
- Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents (IVA)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper presents a computational study to analyze and predict turns (i.e., turn-taking and turn-keeping) in multiparty conversations. Specifically, we use a high-fidelity hybrid data acquisition system to capture a large-scale set of multi-modal natural conversational behaviors of interlocutors in three-party conversations, including gazes, head movements, body movements, speech, etc. Based on the inter-pausal units (IPUs) extracted from the in-house acquired dataset, we propose a transformer-based computational model to predict the turns based on the interlocutor states (speaking/back-channeling/silence) and the gaze targets. Our model can robustly achieve more than 80% accuracy, and the generalizability of our model was extensively validated through cross-group experiments. Also, we introduce a novel computational metric called “relative engagement level" (REL) of IPUs, and further validate its statistical significance between turn-keeping IPUs and turn-taking IPUs, and between different conversational groups. Our experimental results also found that the patterns of the interlocutor states can be used as a more effective cue than their gaze behaviors for predicting turns in multiparty conversations.more » « less
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Participants in a conversation must carefully monitor the turn-management (speaking and listening) willingness of other conversational partners and adjust their turn-changing behaviors accordingly to have smooth conversation. Many studies have focused on developing actual turn-changing (i.e., next speaker or end-of-turn) models that can predict whether turn-keeping or turn-changing will occur. Participants' verbal and non-verbal behaviors have been used as input features for predictive models. To the best of our knowledge, these studies only model the relationship between participant behavior and turn-changing. Thus, there is no model that takes into account participants' willingness to acquire a turn (turn-management willingness). In this paper, we address the challenge of building such models to predict the willingness of both speakers and listeners. Firstly, we find that dissonance exists between willingness and actual turn-changing. Secondly, we propose predictive models that are based on trimodal inputs, including acoustic, linguistic, and visual cues distilled from conversations. Additionally, we study the impact of modeling willingness to help improve the task of turn-changing prediction. To do so, we introduce a dyadic conversation corpus with annotated scores of speaker/listener turn-management willingness. Our results show that using all three modalities (i.e., acoustic, linguistic, and visual cues) of the speaker and listener is critically important for predicting turn-management willingness. Furthermore, explicitly adding willingness as a prediction task improves the performance of turn-changing prediction. Moreover, turn-management willingness prediction becomes more accurate when this joint prediction of turn-management willingness and turn-changing is performed by using multi-task learning techniques.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3472306.3478360
